UI/UX Design Principles

🌎 Designing Cross-Cultural And Multi-Lingual UX. Guidelines on how to stress test our designs, how to define a localization strategy and how to deal with currencies, dates, word order, pluralization, colors and gender pronouns. ⦿ Translation: “We adapt our message to resonate in other markets”. ⦿ Localization: “We adapt user experience to local expectations”. ⦿ Internationalization: “We adapt our codebase to work in other markets”. ✅ English-language users make up about 26% of users. ✅ Top written languages: Chinese, Spanish, Arabic, Portuguese. ✅ Most users prefer content in their native language(s). ✅ French texts are on average 20% longer than English ones. ✅ Japanese texts are on average 30–60% shorter. 🚫 Flags aren’t languages: avoid them for language selection. 🚫 Language direction ≠ design direction (“F” vs. Zig-Zag pattern). 🚫 Not everybody has first/middle names: “Full name” is better. ✅ Always reserve at least 30% room for longer translations. ✅ Stress test your UI for translation with pseudolocalization. ✅ Plan for line wrap, truncation, very short and very long labels. ✅ Adjust numbers, dates, times, formats, units, addresses. ✅ Adjust currency, spelling, input masks, placeholders. ✅ Always conduct UX research with local users. When localizing an interface, we need to work beyond translation. We need to be respectful of cultural differences. E.g. in Arabic we would often need to increase the spacing between lines. For Chinese market, we need to increase the density of information. German sites require a vast amount of detail to communicate that a topic is well-thought-out. Stress test your design. Avoid assumptions. Work with local content designers. Spend time in the country to better understand the market. Have local help on the ground. And test repeatedly with local users as an ongoing part of the design process. You’ll be surprised by some findings, but you’ll also learn to adapt and scale to be effective — whatever market is going to come up next. Useful resources: UX Design Across Different Cultures, by Jenny Shen https://lnkd.in/eNiyVqiH UX Localization Handbook, by Phrase https://lnkd.in/eKN7usSA A Complete Guide To UX Localization, by Michal Kessel Shitrit 🎗️ https://lnkd.in/eaQJt-bU Designing Multi-Lingual UX, by yours truly https://lnkd.in/eR3GnwXQ Flags Are Not Languages, by James Offer https://lnkd.in/eaySNFGa IBM Globalization Checklists https://lnkd.in/ewNzysqv Books: ⦿ Cross-Cultural Design (https://lnkd.in/e8KswErf) by Senongo Akpem ⦿ The Culture Map (https://lnkd.in/edfyMqhN) by Erin Meyer ⦿ UX Writing & Microcopy (https://lnkd.in/e_ZFu374) by Kinneret Yifrah

3 out of 5 product pages I work on have bounce rates above 70%. Most often, this is due to - 1. Low-quality ad traffic 2. Poor UX on the PDP  3. Basic, non-engaging product images  4. Lack of information / USPs about the product  5. First fold of the PDP not being optimized If you're sure that your targeting is on-point and that good quality traffic lands on your PDPs, then reducing your bounce rate should be the #1 priority. As, a lower bounce rate equals better conversions and higher revenue. In this example, using Mothercare PLC's PDP, I’ve implemented changes that can reduce the bounce rate by building trust in the brand and the product. Below are the 6 changes I recommend a/b testing - 1. Moving the product name in the first fold along with other details like reviews, price. In this case, I've also changed the product name a little, adding 'Pack of 3' which creates value for the amount they're paying. 2. Add a lifestyle image of your product being worn or used. More important for fashion brands where size is a common concern. 3. Add key USPs about the product. Especially in this case where the parent wants to know whether it's a good material and easy to change. 4. Add info on what size the baby is wearing, enabling the shopper to be more confident about their sizing decision. More important here as baby clothing is often bought by relatives and friends and gifted at baby showers, birthdays. 5. Replacing the wishlist with a 'Buy Now' CTA as that can help the user checkout immediately. 6. Optimizing the area around the add to cart by adding information on the shipping timeline, free shipping, and returns. Add a prominent 'View offers' option which can motivate users to complete their purchase. Other than that, I've implemented the following UX changes to improve the shopping experience: 1. Improved the image browsing experience by adding thumbnails and a sneak peek of the next image. 2. Made the size selection easier and more intuitive. Changing the section's title to 'Select Size', and showing a prominent, selected size by default. 3. Increased the CTA size for Add to Cart and Buy Now. Found this useful? Let me know in the comments! P.S. Being able to identify these minor details is a skill worth developing. The best way to do this is by looking at competitor websites. Carefully observing what elements they use on their PDPs can help you understand the industry and its requirements better.

Poka Yoke is a Japanese term that translates to "mistake-proofing" or "error-proofing." It is a concept used in manufacturing and process design to prevent errors by designing systems, tools, or processes that make it impossible or extremely difficult to make mistakes. The goal of Poka Yoke is to eliminate defects by addressing the root causes of errors, ensuring quality and efficiency. #Key Principles of Poka Yoke: 1. Prevention: Designing processes or tools to prevent errors before they occur. 2. Detection: Identifying and correcting errors as soon as they happen. 3. Simplicity: Making the solution easy to implement and use. #Examples of Poka Yoke: - Physical Design: USB ports are designed so they can only be inserted one way, preventing incorrect connections. - Process Checks: A machine that stops if a step is skipped or a part is missing. - Visual Indicators: Color-coding or labeling to ensure correct assembly or usage. #Benefits of Poka Yoke: - Reduces defects and waste. - Improves product quality and reliability. - Saves time and costs by avoiding rework. - Enhances safety by preventing human error. Poka Yoke is a key component of Lean Manufacturing and Six Sigma methodologies, emphasizing continuous improvement and efficiency. It was developed by Shigeo Shingo, a Japanese industrial engineer, in the 1960s.

As we transition from traditional task-based automation to 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀, understanding 𝘩𝘰𝘸 an agent cognitively processes its environment is no longer optional — it's strategic. This diagram distills the mental model that underpins every intelligent agent architecture — from LangGraph and CrewAI to RAG-based systems and autonomous multi-agent orchestration. The Workflow at a Glance 1. 𝗣𝗲𝗿𝗰𝗲𝗽𝘁𝗶𝗼𝗻 – The agent observes its environment using sensors or inputs (text, APIs, context, tools). 2. 𝗕𝗿𝗮𝗶𝗻 (𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗘𝗻𝗴𝗶𝗻𝗲) – It processes observations via a core LLM, enhanced with memory, planning, and retrieval components. 3. 𝗔𝗰𝘁𝗶𝗼𝗻 – It executes a task, invokes a tool, or responds — influencing the environment. 4. 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (Implicit or Explicit) – Feedback is integrated to improve future decisions.     This feedback loop mirrors principles from: • The 𝗢𝗢𝗗𝗔 𝗹𝗼𝗼𝗽 (Observe–Orient–Decide–Act) • 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 used in robotics and AI • 𝗚𝗼𝗮𝗹-𝗰𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝗲𝗱 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 in agent frameworks Most AI applications today are still “reactive.” But agentic AI — autonomous systems that operate continuously and adaptively — requires: • A 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗹𝗼𝗼𝗽 for decision-making • Persistent 𝗺𝗲𝗺𝗼𝗿𝘆 and contextual awareness • Tool-use and reasoning across multiple steps • 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 for dynamic goal completion • The ability to 𝗹𝗲𝗮𝗿𝗻 from experience and feedback    This model helps developers, researchers, and architects 𝗿𝗲𝗮𝘀𝗼𝗻 𝗰𝗹𝗲𝗮𝗿𝗹𝘆 𝗮𝗯𝗼𝘂𝘁 𝘄𝗵𝗲𝗿𝗲 𝘁𝗼 𝗲𝗺𝗯𝗲𝗱 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 — and where things tend to break. Whether you’re building agentic workflows, orchestrating LLM-powered systems, or designing AI-native applications — I hope this framework adds value to your thinking. Let’s elevate the conversation around how AI systems 𝘳𝘦𝘢𝘴𝘰𝘯. Curious to hear how you're modeling cognition in your systems.

Product managers & designers working with AI face a unique challenge: designing a delightful product experience that cannot fully be predicted. Traditionally, product development followed a linear path. A PM defines the problem, a designer draws the solution, and the software teams code the product. The outcome was largely predictable, and the user experience was consistent. However, with AI, the rules have changed. Non-deterministic ML models introduce uncertainty & chaotic behavior. The same question asked four times produces different outputs. Asking the same question in different ways - even just an extra space in the question - elicits different results. How does one design a product experience in the fog of AI? The answer lies in embracing the unpredictable nature of AI and adapting your design approach. Here are a few strategies to consider: 1. Fast feedback loops : Great machine learning products elicit user feedback passively. Just click on the first result of a Google search and come back to the second one. That’s a great signal for Google to know that the first result is not optimal - without tying a word. 2. Evaluation : before products launch, it’s critical to run the machine learning systems through a battery of tests to understand in the most likely use cases, how the LLM will respond. 3. Over-measurement : It’s unclear what will matter in product experiences today, so measuring as much as possible in the user experience, whether it’s session times, conversation topic analysis, sentiment scores, or other numbers. 4. Couple with deterministic systems : Some startups are using large language models to suggest ideas that are evaluated with deterministic or classic machine learning systems. This design pattern can quash some of the chaotic and non-deterministic nature of LLMs. 5. Smaller models : smaller models that are tuned or optimized for use cases will produce narrower output, controlling the experience. The goal is not to eliminate unpredictability altogether but to design a product that can adapt and learn alongside its users. Just as much as the technology has changed products, our design processes must evolve as well.

We are not yet ready for this. A growing army of autonomous agents are engaging with not just humans and other agents, but also economic and legal institutions. An "agent infrastructure" of systems and protocols could maximize benefits and contain risks, suggest a group of researchers from Centre for the Governance of AI (GovAI) Harvard Law School University of Oxford University of Cambridge and others (link in comments). Most AI safety research is focused on AI system-level interventions. However different approaches are required in a proliferating multi-agent environment. The researchers propose 3 major functions in effective agent infrastructure: Attribution, Interaction, and Response: 💡 Attribution: Ensuring accountability. Attribution is critical for linking AI agent actions to responsible parties, such as users or organizations. Mechanisms including identity binding, to associate an agent’s actions with a legal entity. Certification provides verifiable assurances about an agent’s behavior, such as data handling policies or autonomy levels. Implementing agent IDs enables tracking and monitoring specific agents, facilitating incident response and accountability. 🤝 Interaction: Shaping behaviors. Interaction infrastructure defines how agents engage with the world to enable reliability and security. Dedicated agent channels isolate agent activities from regular digital traffic, reducing risks like data contamination or accidental disruptions. Oversight layers empower users or managers to intervene when necessary, improving operational control and accountability. Inter-agent communication protocols support seamless collaboration and negotiation among agents, promoting cooperative outcomes in multi-agent systems. 🔄 Response: Mechanisms to mitigate harm. Response infrastructure addresses problems caused by agents using proactive and reactive measures. Incident reporting systems collect detailed data on harmful events, enabling developers and regulators to understand root causes and implement safeguards. Rollback mechanisms allow reversal of unintended actions, such as erroneous financial transactions, protecting users from significant harm. The concept of agent infrastructure and proposed framework provide a very useful framework to build the next phase of scalable agent ecosystems. We need to develop and agree on these principles soon, as the foundations of a burgeoning agent economy will be built through this year.

A/B Testing in Google Ads: Best Practices for Better Performance Introduction to A/B Testing A/B testing in Google Ads is a crucial strategy for optimizing ad performance through data-driven insights. It involves comparing two versions of an ad to determine which one delivers better results.  Set Clear Goals Before conducting A/B tests, define clear objectives such as increasing click-through rates or conversions. Having specific goals will guide your testing process and help you measure success accurately.  Test Variables To effectively A/B test ads, focus on testing one variable at a time, such as the ad copy, images, or call-to-action. This approach will provide clear insights into what elements are driving performance. Create Variations Develop distinct ad variations with subtle differences to compare their impact. Ensure that each version is unique enough to produce measurable results but relevant to your target audience.  Implement Proper Tracking Set up conversion tracking and monitor key metrics closely to evaluate the performance of each ad variation accurately. Use tools like Google Analytics to gather meaningful data. Monitor Performance Metrics Regularly review performance metrics like click-through rates, conversion rates, and cost per acquisition to identify trends and patterns. Analyzing these metrics will help you make informed decisions. Scale Successful Tests Once you identify a winning ad variation, scale it by allocating more budget and resources to drive maximum results. Replicate successful strategies in future campaigns. Continuous Optimization Optimization is an ongoing process, so continue to test, refine, and adapt ad elements to enhance performance continuously. Stay updated with industry trends and consumer preferences. Analyze Results After conducting A/B tests, analyze the results comprehensively to understand the impact of your optimizations. Use the insights gained to inform future ad strategies. Summary  Following best practices for A/B testing in Google Ads can significantly improve the performance of your campaigns. By testing, analyzing, and optimizing ad variations, you can enhance engagement, conversions, and overall ROI. #MetaAds, #VideoMarketing, #DigitalAdvertising, #SocialMediaStrategy, #ContentCreation, #BrandAwareness, #VideoBestPractices, #MarketingTips, #MobileOptimization, #AdPerformance

𝗛𝘂𝗺𝗮𝗻-𝗖𝗲𝗻𝘁𝗲𝗿𝗲𝗱 𝗗𝗲𝘀𝗶𝗴𝗻 𝗶𝗻 𝗔𝗰𝘁𝗶𝗼𝗻 Design goes beyond aesthetics—it's about functionality and user experience. It’s not just about making products look good; it's about how they work seamlessly in our daily lives. From the intuitive interface of the iPhone to the ergonomic design of the MacBook, we all know how Apple exemplifies Steve Jobs' belief that design is not just appearance. Great design is how it works. Our world is making a conscious shift towards human-centered design, an approach where the user is at the heart of the design process. Here’s a great case study that shows how empathy is at the core of design thinking. This approach ensures that when creating an application, product, or service, you prioritize the end-users' needs and perspectives from the very beginning of the ideation process. A children's toothbrush that remains popular today was developed through a collaboration between Oral-B and the global design firm IDEO in the mid-nineties. Instead of merely replicating existing products—a scaled-down version of an adult toothbrush—IDEO took a more insightful approach by observing children in the act of brushing their teeth. The observation revealed a significant challenge: children struggled to grip the slim toothbrush handles designed for adults due to their limited motor skills. Recognizing this, IDEO's team innovated a toothbrush with a larger, more ergonomic grip that was easier for children to hold. Every toothbrush company worldwide now produces similar designs.  A modern essential that has become almost a generational staple is the Dyson vacuum cleaner. Unlike traditional vacuums, which were often stashed away in closets due to their long, tangled cords, Dyson's sleek, cordless designs are meant to be prominently displayed and proudly showcased, not hidden away – another design win! The same goes for the Dyson hair dryer. The Dyson engineering team attended beauty school to better understand how hair dryers are used. The result was the Dyson Supersonic – a hairdryer with the tiniest motor and the Heat Shield Technology. As you can see, human-centered design is a creative approach to business problem solving. It leverages the designer's toolkit to seamlessly integrate people's needs, technological opportunities, and business imperatives.  Picture Credit: Chapter247      

Let’s say you’re a marketer hoping to win traffic from anyone searching for the "Best Beatles Songs." In the past, your SEO strategy would be to target keywords, and create content with corresponding headlines. i.e. “Must-Listen Beatles Songs” But now you need a different game plan. As we see more and more AI-powered engines like Perplexity and ChatGPT enter the market, the way we find information is becoming drastically different. These companies are making rev-share deals with major publishers to ensure their models have current, fresh information that’s accurate, comprehensive and forward thinking. To win an AI-enhanced search, your content should address the question: why are people searching for Beatles’ songs in the first place? You need to consider broader context and user intent. For example, are users discovering The Beatles for the first time and looking for an introduction to their catalog, or are they superfans wanting deeper insights into the music’s impact on culture? Offer value that goes beyond listing songs—provide historical context, trivia, or playlists curated for different moods or occasions. Focus on interactive or multimedia content, such as videos, audio clips, or even AI-generated playlists to create a richer, more engaging user experience. Show the search engine that your content satisfies not just the initial question, but also the deeper exploration the user might engage in. By doing this, you position yourself to build a trusted relationship with users.

💡User Journey Map vs User Flow: When & how to use tools Understanding how users interact with your product is critical for creating engaging experiences. User journey map and user flow are two essential tools for this purpose . While both tools help visualize user interactions, they serve different purposes. 🍎 User journey map A user journey map visualizes the user's experiences & emotions while interacting with a product or service. It highlights pain points, motivations, and touchpoints across the entire journey, from awareness to post-purchase. Components of user journey map: ✔ Stages: Different phases a user goes through (e.g., awareness, consideration, decision, purchase, retention). ✔ Touchpoints: Interactions between the user and the product or service (e.g., visiting a website, contacting support). ✔ Actions: Actions the user takes at each stage (e.g., filling out a product request form) ✔ Emotions: How user feel at each touchpoint (both positive and negative emotions) ✔ Pain Points: Challenges faced by the user ✔ Opportunities: Potential areas for improvement Use cases for user journey maps: ✔ Identifying user pain points and areas for improvement in customer experience   ✔ Aligning teams on user-centric strategies (putting strong focus on user) 📺 How to design user journey map in FigJam (YouTube): https://lnkd.in/djJR6by8 🍏 User flow A user flow diagram focuses on the specific steps and interactions a user takes to complete a particular task within a product or service. Components of user flow: ✔ Entry point: How the user begins the flow (e.g., landing on the homepage of an online shop). ✔ Steps: Sequential actions the user takes to complete the task (e.g., browsing product categories, adding a product to cart, checking out). ✔ Decision points: Moments where the user must make a choice (e.g., selecting a payment method). ✔ Exit point: The end of the flow where the user accomplishes their goal (e.g., order confirmation). Use cases: ✔ Designing and optimizing specific user tasks (e.g., checkout flow) ✔ Facilitating usability testing and feedback (prioritizing test cases for the flow) 📺 How to design user flow in FigJam (YouTube): https://lnkd.in/dcCnAH6R 📕 3 Key Differences between user journey and user flow: 1️⃣ Scope: User journey map: Broad, covers the entire user experience across multiple touchpoints and stages. User flow: Narrow, focuses on specific tasks and interactions within a product. 2️⃣ Focus: User journey map: Emphasizes user emotions, pain points, and overall experience. User flow: Emphasizes efficiency and logical progression of tasks. 3️⃣ Aim: User journey map: Ideal for understanding the user's holistic experience and identifying strategic opportunities for improvement. User flow: Ideal for designing and refining specific features or processes within a product. #UX #UI #design #uxdesign #uidesign #productdesign